Tarea 2

Ejercicio 1

rm(list = ls())
library(sf)

## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE

linkWorld_gpkg="https://github.com/krlos20203563/ASIE_2024-1/raw/main/data_TA2/maps/worldMap.gpkg"
sf::st_layers(linkWorld_gpkg)

## Driver: GPKG 
## Available layers:
##       layer_name geometry_type features fields crs_name
## 1 countryBorders                    252      1   WGS 84
## 2     riverLines                     98      2   WGS 84
## 3     cityPoints         Point      610      3   WGS 84

countries=read_sf(linkWorld_gpkg,layer="countryBorders")
rivers=read_sf(linkWorld_gpkg,layer="riverLines")
cities=read_sf(linkWorld_gpkg,layer="cityPoints")

##Mapa del mundo

library(plotly)

## Cargando paquete requerido: ggplot2

## 
## Adjuntando el paquete: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

baseLayer <- plot_ly() %>%
  add_sf(data = countries, fill = I('grey90'), color = I('black'), name = 'Countries', text = ~COUNTRY, hoverinfo = 'text')

final <- baseLayer %>%
  add_sf(data = rivers, color = I('blue'), name = 'Rivers', text = ~NAME, hoverinfo = 'text', inherit = FALSE) %>%
  add_sf(data = cities, color = I('red'), name = 'Cities', text = ~NAME, hoverinfo = 'text', inherit = FALSE) %>%
  layout(
    title = 'Mapa interactivo',
    xaxis = list(title = 'Longitud'),
    yaxis = list(title = 'Latitud'),
    plot_bgcolor = 'lightgrey'
  )

final

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

Ejercicio 2

library(sf)
peru_gpkg="https://github.com/krlos20203563/ASIE_2024-1/raw/main/data_TA2/maps/peru.gpkg"
sf::st_layers(peru_gpkg)

## Driver: GPKG 
## Available layers:
##    layer_name geometry_type features fields crs_name
## 1 peruBorders       Polygon        1      1   WGS 84
## 2  peruRivers                      5      2   WGS 84
## 3  peruCities         Point        8      3   WGS 84

library(sf)
peru=read_sf(peru_gpkg,layer="peruBorders")
peru_rivers=read_sf(peru_gpkg,layer="peruRivers")
peru_cities=read_sf(peru_gpkg,layer="peruCities")

Mapa del Perú

basePeru <- plot_ly() %>%
  add_sf(data = peru, fill = I('grey90'), color = I('black'), name = 'Countries', text = ~COUNTRY, hoverinfo = 'text')

peruMap <- basePeru %>%
  add_sf(data = peru_rivers, color = I('blue'), name = 'Rivers', text = ~NAME, hoverinfo = 'text', inherit = FALSE) %>%
  add_sf(data = peru_cities, color = I('red'), name = 'Cities', text = ~NAME, hoverinfo = 'text', inherit = FALSE) %>%
  layout(
    title = 'Mapa interactivo',
    xaxis = list(title = 'Longitud'),
    yaxis = list(title = 'Latitud'),
    plot_bgcolor = 'lightgrey'
  )

peruMap

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

#Ejercicio 3

link_rpy_gpkg="https://github.com/SocialAnalytics-StrategicIntelligence/GeoDFBasics_py/raw/main/maps/peruMap_5389.gpkg"
sf::st_layers(link_rpy_gpkg)

## Driver: GPKG 
## Available layers:
##   layer_name geometry_type features fields              crs_name
## 1   airports         Point      197     18 Peru96 / UTM zone 19S
## 2     border       Polygon        1      1 Peru96 / UTM zone 19S
## 3     rivers                      5      2 Peru96 / UTM zone 19S
## 4     cities         Point        8      3 Peru96 / UTM zone 19S

peru_rep=read_sf(link_rpy_gpkg,layer="border")
peru_rivers_rep=read_sf(link_rpy_gpkg,layer="rivers")
peru_cities_rep=read_sf(link_rpy_gpkg,layer="cities")
peru_airports=read_sf(link_rpy_gpkg,layer="airports")

Mapa del Perú con aeropuertos

base_PE_aeropuertos <- plot_ly() %>%
  add_sf(data = peru_rep, fill = I('grey90'), color = I('black'), name = 'Countries', text = ~COUNTRY, hoverinfo = 'text')

peruMap_air <- base_PE_aeropuertos %>%
  add_sf(data = peru_rivers_rep, color = I('blue'), name = 'Rivers', text = ~NAME, hoverinfo = 'text', inherit = FALSE) %>%
  add_sf(data = peru_cities_rep, color = I('red'), name = 'Cities', text = ~NAME, hoverinfo = 'text', inherit = FALSE) %>%
  add_sf(data = peru_airports, color = I('green'), name = 'Airports', text = ~name, hoverinfo = 'text', inherit = FALSE) %>%
  layout(
    title = 'Mapa interactivo',
    xaxis = list(title = 'Longitud'),
    yaxis = list(title = 'Latitud'),
    plot_bgcolor = 'lightgrey'
  )

peruMap_air

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

Ejercicio 4

https://github.com/krlos20203563/ASIE_2024-1/raw/main/data_TA2/maps/america_frag.gpkg

library(sf)
america_gpkg="https://github.com/krlos20203563/ASIE_2024-1/raw/main/data_TA2/maps/america_frag.gpkg"
sf::st_layers(america_gpkg)

## Driver: GPKG 
## Available layers:
##   layer_name geometry_type features fields crs_name
## 1  fragility                    340     35   WGS 84

america=read_sf(america_gpkg,layer="fragility")

centroids <- st_centroid(america)

## Warning: st_centroid assumes attributes are constant over geometries

ggplot(data = america) +
  geom_sf() +
  geom_sf(data = centroids, aes(color = Total_ei5_cat), size = 3) +
  scale_color_viridis_d(name = "Fragility (Total_ei5)") +
  theme_minimal() +
  labs(title = "Fragility of American Countries (2020)")